Chapter 2 Literature Review

Transcription

1 Chapter 2 Literature Review 2.1 Data Mining The amount of data continues to grow at an enormous rate even though the data stores are already vast. The primary challenge is how to make the database a competitive business advantage by converting seemingly meaningless data into useful information. How this challenge is met is critical because companies are increasingly relying on effective analysis of the information simply to remain competitive. A mixture of new techniques and technology is emerging to help sort through the data and find useful competitive data. By knowledge discovery in databases, interesting knowledge, regularities, or high-level information can be extracted from the relevant sets of data in databases and be investigated from different angles, and large databases thereby serve as rich and reliable sources for knowledge generation and verification. Mining information and knowledge from large database has been recognized by many researchers as a key research topic in database systems and machine learning. Companies in Many industries also take knowledge discovering as an important area with an opportunity of major revenue (Fayyad et al., Piatetsky-Shapiro et al. 1991, Silberschatz et al. 1995). The discovered knowledge can be applied to information management, query processing, decision making, process control, and many other applications. From a data warehouse perspective, data mining can be viewed as an advanced stage of on-line analytical processing (OLAP). However, data mining goes far beyond the narrow scope of summarization-style analytical processing of data warehouse systems by incorporating more advanced techniques for data understanding (Han et. al., 2001). Many people treat data mining as a synonym for another popularly used term, Knowledge Discovery in Databases, or KDD. Alternatively, others view data mining as simply an essential step in the process of knowledge discovery in databases. For example, Fayyad stated KDD process as follows (1996): 1. Learning the application domain 2. Creating a target dataset 3. Data cleaning and preprocessing 4. Data reduction and projection 5. Choosing the function of data mining 6. Choosing the data mining algorithm(s) 7. Data mining 5

2 8. Interpretation 9. Using the discovered knowledge As the KDD process shows, data mining is the central of knowledge discovering, it requires complicated data preparation works. Data cleaning and preprocessing: includes basic operations, such as removing noise or outliers, collecting the necessary information to model or account for noise, deciding on strategies for handling missing data fields, and accounting for time sequence information and known changes, as well as deciding DBMS issues, such as data types, schema, and mapping of missing and unknown values. Useful data are chosen from the formatted data to increase the effectiveness and focus on the task. After data preparation, choosing the function of data mining decide the purpose of the model derived by data mining algorithm (e.g. classification, clustering, and summarization). Choosing the data mining algorithm includes selecting method(s) to be used for searching for patterns in the data, such as deciding which models and parameters may be appropriate and matching a particular data mining method with the overall criteria of the KDD process. Data mining searches for patterns interest in a particular representational form set of such representations, including classification rules or trees, regression, clustering, sequence modeling, dependency, and line analysis. The mining results which match the requirements will be interpreted and marshaled, to be taken into action or be presented to interested parties in the final step. For the importance of data mining in KDD process, the term data mining is becoming more popular than the longer term of knowledge discovery (Han, 2001). People gradually adapt a broad view of data mining functionality to be the synonym of KDD. The concept of data mining contains all activities and techniques utilizing the collected data to get implicit information and analyzing historical records to gain valuable knowledge Data Mining Definition Simply stated, data mining refers to extracting or mining knowledge from large amounts of data (Han et. al., 2001). Data mining is the process of exploration and analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns and rules (Berry and Linoff, 2000).The SAS Institute (2000) defines data mining as the process of selecting, exploring and 6

3 modeling large amounts of data to uncover previously unknown data patterns for business advantage. Data mining refers to as knowledge discovery in databases, means a process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases (Piatetsky-Shapiro, et al., 1991). From the business perspective, various data mining techniques are used to better understand user behavior, to meliorate the service provided, and to increase the business opportunities (Chen et. al., 1996). Whatever the definition, the data mining process differs, as underlined by Sato (2000), from the statistical analysis of data. First, predictive data mining is governed by the need to uncover, in a timely manner, emerging trends, whereas statistical analysis is related to historical facts and based on observed data. Second, statistical analysis focuses on finding and explaining the major sources of variation in the data, in contrast, data mining endeavors to discover, not the obvious sources of variation, but rather the meaningful, although currently overlooked, information. Therefore, statistical analysis and data mining are complementary. Statistical analysis explains and removes the major part of data variation before data mining is used. This explains why the data warehousing tool not only stores data but also contains and executes some statistical analysis programs. As to on-line analysis processing (OLAP), its relationship with data mining can be viewed as disjoint (Han, 2001). OLAP is a data summarization/aggregation tool that helps simplify data analysis, while data mining allows the automated discovery of implicit patterns and interesting knowledge hidden in large amounts of data. OLAP tools are targeted toward supporting and simplifying interactive data analysis; while data mining tools is to automate as much of the analysis process as possible. Data mining goes one step beyond OLAP. As mentioned in the previous section, data mining is almost equal to KDD and they have similar process. Here are the data mining processes described by Anand et. al. (1997): 1. human resource identification 2. problem specification 3. data prospecting 4. domain knowledge elicitation 5. methodology identification 6. data pre-processing 7. pattern discovery 8. knowledge post-processing 7

4 In stage 1 of data mining process, human resource identification, the human resource should be involved in the project and their respective roles are identified. In most data mining problems the human resources required are the domain expert, the data expert and the data mining expert. In stage 2, interested problems are analyzed and specified. Next, data prospecting involves in analyzing the available data and choosing the promising subset of data to mine. The purpose of stage 4, domain knowledge elicitation, is to elicit the useful knowledge already known about the problem from domain experts. In stage 5, methodology identification, the most appropriate mining paradigms are chosen. In stage 6, data pre-processing is described to transform data into the state that is fit for mining. Pattern discovery stage which includes the computation and knowledge discovery is discussed in section 7. The rules found in the previous stage are filtered to draw the best rules in the last stage. Fayyad et al. (1996), on the other hand, proposed the following steps: 1. Retrieving the data from a large database. 2. Selecting the relevant subset to work with. 3. Deciding on the appropriate sampling system, cleaning the data and dealing with missing fields and records. 4. Applying the appropriate transformations, dimensionality reduction, and projections. 5. Fitting models to the preprocessed data. The procedures of data mining are detailed and complicated. Many essentials should be noticed during the flow of data mining, so challenges of developing data mining applications are one of the important issues in this area. Chen et. al. (1996) listed the challenges of development as follows: 1. Handling of different types of data 2. Efficiency and scalability of data mining algorithm 3. Usefulness, certainty, and expressiveness of data mining results 4. Expression of various kinds of data mining results 5. Interactive mining knowledge at multiple abstraction levels 6. Mining information from different sources of data 7. Protection of privacy and data security Data Mining Structure The architecture of a typical data mining system may have the following major 8

5 components (Han and Kamber, 2001) 1. Database, data warehouse or other information repository 2. Database or data warehouse server 3. Knowledge base 4. Data mining engine 5. Pattern evaluation module 6. Graphical user interface The data sources of a data mining system can be divergent information repositories like database, data warehouse, or other repository. The database or data warehouse server is responsible for fetching the relevant data to fulfill the data mining request. Data mining engine is the core of data mining system. The functional modules of Data mining algorithms and rules are kept in the engine. Knowledge base stores the domain knowledge that is used to guide the data mining process, and provides the data that pattern evaluation module needs to validate the results of data mining. If the mining results has past the validation step then they will be bring to user via the graphical user interface and users can interact with the system by the interface Data Mining Method Data mining methods refers to the function types that data mining tools provide. The conceptual definition of each data mining method and the assortment basis always differ for the ease of explanation, the consideration of present situation, or researcher s background. Classification, association, prediction, clustering are usually the common methods in different works, while the term description, summarization, sequential rule etc. may not always be used and listed in the first place. If some methods are not listed, it does not denote these methods uncreated because the researcher may assign special terms to methods to show certain important characters. Industry specification and problems sector can also be a good reason to reconsider the terminology. For example, regression is often used to replace the term prediction because the major and traditional techniques of prediction are statistical regression. Link analysis can be discussed independently outlying association in telecommunication industry. Table 2-1 shows the methods recognized by scholars: Data mining methods include techniques which evolve from artificial intelligence, statistics, machine learning, OLAP and so on. These most often mentioned methods are classified into five categories according to their function types 9

6 in business applications, and the five types of data mining methods are: association, classification, prediction, clustering, and profiling. Table 2-1 Data Mining Categorization Literatures Source: This research Author Data Mining Categorization Fayyad, et. (1996) Classification, Regression, Clustering, Summarization, Dependency modeling, Link analysis, Sequence analysis Han et. al. (1996) Association, Generalization, Classification, Clustering, Similarity search, Path traversal pattern Berry (1997) Classification, Estimation, Prediction, Affinity grouping, Clustering, Description Association: discovers relationship or dependency between multiple things, such as link analysis, market basket analysis, and variables dependency. Association exists at two levels: structured and quantitative. The structural level of the method specifies (often in graphical form) which things are related; the quantitative level specifies the strengths of the relationship using some numerical scale. Market basket analysis is a well known association application; it can be performed on the retail data of customer transactions to find out what items are frequently purchased together (A.K.A. itemsets). Apriori is the basic algorithm for finding frequent itemsets. The extension of Apriori can further handle multi-level, multi-dimensional, and more complex data structure. Classification: maps (or classifies) a data item into one of several predefined categorical classes. Decision tree, neural network, and some probability approaches are often used to perform this function. There are two steps to implement classification function. In the first step, classification model is build describing a predetermined set of classes or concepts. Second step, the model is used for classification. For example, the classification rules learned in the first step from the analysis of data from existing customers can be used to predict the credit rating of new or future customers. Prediction: includes regression and part of time-series analysis. Prediction can be viewed as the construction and use of a model to assess the value or value ranges of an attribute that a given sample is likely to have. This method maps a data item to a real-value prediction variable, and the goal of time-series analysis is to model the 10

7 states of the process generating the sequence or to extract and report deviation and trends over time. In our view, the major difference between prediction and classification is that prediction processes with continuous values while classification focuses on judgments. Clustering: maps a data item into one of several categorical classes (or clusters) in which the classes must be determined from the data-unlike classification in which the classes are predefined. Clusters are defined by finding natural grouping of data item based on similarity metrics or probability density models, and the process to build these groups is named as unsupervised learning to distinguish from supervised learning of classification (Han and Kamber, 2001) Data Mining Modeling Data mining modeling is the critical part in developing business applications. Business applications, such as cross selling, will be turn into one or more of business problems, and the goal of modeling is to formulate these business problems as a data mining tasks. For example, in cross selling application, the association in the product area is determined, and then customers will be classified into several clusters to see which product mix can be matched to what customers. To know which data mining task is most suitable for current problem, the analysis and understanding of data mining task s characters and steps is needed. Fayyad (1996) states that data mining algorithms consists largely of some specific mix of three components: 1 The model: There are two relevant factors: the function of the model (e.g., classification and clustering) and the representational form of the model (e.g., a linear function of multiple variables and a Gaussian probability density function). A model contains parameters that are to be determined from the data. 2 The preference criterion: A basis for preference of one model or set of parameters over another, depending on the given data. The criterion is usually some form of goodness-of-fit function of the model to the data, perhaps tempered by a smoothing term to avoid overfitting, or generating a model with too many degrees of freedom to be constrained by the given data. 3 The search algorithm: The specification of an algorithm for finding particular models and parameters, given data, a model (or family of models), and a preference criterion. 11

8 In fact, the selection problems of many business applications already have regular answers. In Berry and Linoff s work (1997), practices of mapping between business tasks and data mining techniques had been done. Table 2-2 Which Techniques to Use for Which Tasks Source: Berry and Linoff, 1997, Data Mining Techniques: for marketing, sales, and customer support, John Eilry # Sons, Inc. Technique Classification Estimation Prediction Affinity Clustering Description Grouping Standard Statistics ˇ ˇ ˇ ˇ ˇ ˇ Market Basket ˇ ˇ ˇ ˇ Analysis Memory-Base d Reasoning ˇ ˇ ˇ ˇ Genetic Algorithms ˇ ˇ Cluster Detection ˇ Link Analysis ˇ ˇ ˇ Decision Trees ˇ ˇ ˇ ˇ Neural Networks ˇ ˇ ˇ ˇ The choice of what data mining techniques to apply at a given point in the knowledge discovery processes depends on the particular data mining task to be accomplished and on the data available for analysis. The requirements of tasks dedicate to the functions of mining, and the detail characteristics of tasks influence the feasibility between mining methods and business problems. The so called detail characteristics include data types, parameter varieties, hybrid approaches and so on. Slightly difference in the model will cause enormous performance change, so modeling stage effects the quality of data mining tools heavily. 2.2 Business Applications Data mining has been applied to countless areas, from medicine, biology, engineering management, digital content, to the most flourished commercial area. In this research, we focus on commercial problems. The industries we chose include: finance and insurance, retail, telecommunication, manufacture, and security investment. Data mining assists many kinds of analysis works in each area, and the 12

9 most popular business applications collected from themes and journals are listed as follows: Finance and insurance 1. Financial product cross-selling: identifies additional types of products that customers could purchase, which they currently are not purchasing. For example, a saving account customer would like to buy life insurance products of the original company. 2. Telemarketing and direct mail marketing: need marketing name list, so data mining is in charge of figuring out the most responsive and valuable customers for certain products. 3. Market segment analysis: is one of the basic marketing researches where similar customers will be divides into same segmentations. 4. Product mix analysis: considers customer needs, marketing strategy, and product line completeness to find out the best product mix. 5. Credit card fraud detection and bad debt collection: monitors and detects unusual activities of fraud and the occurrence of bad debt. 6. Customer churn analysis: is used to analysis the transaction record and behavior pattern to know the possibility of customer attrition, and then, company will apply different recovery actions to customers base on their churn tendency and contribution to the company. 7. Credit card application and personal loan rating: classifies the customer into different level for loan or credit card applications. Retail 1. Member customer cross-selling: analyzes customer s transaction records to further introduce them other products. 2. Promotional product mix: discovers the items that often be purchased together, so these products can be placed closely or be sold in pairs. 3. Advertisement name list analysis: uses analytical tools to generate the customer list from the customer database for marketing activity to get a higher responsive rate and reduce marketing costs. 4. Market segmentation: divides the market into several parts by certain characters. 5. Direct marketing: needs analytical software to figure out the most responsive and valuable customers for marketing. 6. Inventory analysis: analyzes inventory and sales data to calculate the trend of future requirement. This information can be the accordance of stock replenishing and marketing decision making. 13

10 Telecommunication 1. Customer segmentation: see Retail (4) 2. Direct marketing: see Retail (5) 3. Charge making: is important to telecommunication companies because the charge policy is the key point which customers concern. The basic objective of charge making application is to design charge policies according to customers calling pattern. 4. Link analysis: associates customers with their communicators. The activities radiate from single customer will link several persons. The relationship among these people can be roughly described and the calling pattern can be used to design more customize charge policy. 5. Customer retention: tracks customer retention rate and analyses customer behavior change to support customer relationship management. Manufacturing 1. Production yield analysis: uses prediction methods to detect defect clusters on product quickly to improve the yield and to reduce the production failure lost. 2. Production process control: finds out the factors in the processes that cause failure and classify factors which cause defect into groups to analyze the quality issues. Security investment 1. Stock and foreign exchange market prediction: builds a simulation model to Predict the fluctuation of stock price and foreign exchange 2. Direct marketing: see Finance and insurance (2). Five industry sectors above each have several data mining applicable applications, and the total amount of applications is twenty-two. In our comprehension, some sectors have similar or duplicate application requirements. The difference of these similar applications lies mainly in domain data, but not in the concept or theory of processing. So we will discuss these similar applications together to reduce the duplication in later chapters. 2.3 Business Application Related Works The primary goal of data mining is to discovery new patterns and deeper insights within the data. New pattern discovery is used in marketing to make predictions about buyer behavior, to understand customer preference, and to manage customer 14

12 Cross-selling applications include financial product cross-selling and retail member customer cross-selling. The advantages of cross-selling strategy are threefold. First, targeting customers with products they are more likely to buy should increase sales and therefore increase profits. Second, reducing the amount of people targeted through more selective targeting should reduce costs. Finally, it is an established fact in the financial sector that loyal customers (from the perspective of their length of relationship with the organization) normally possess more than two products on average. Therefore, persuading customers to buy more than one product would increases both sales and customer loyalty. To achieve cross-selling effects, knowing who would be interested in what product is the key. The overall objective was to discover characteristics of current customers, which can then be used to target all other customer segments, in order to classify them into potential promotion targets and unlikely purchasers. There are four component tasks that can be identified for the cross-sales problem. They are (Anand et. al. 1997): 1. Finding sets of attributes that identify customers in the customer base that are most likely to buy a particular product. 2. Choosing the best sets of these sets of attributes to identify customers to target in a marketing campaign of some sort (for example, a mail shot). 3. Carting out this marketing campaign and analyzing the results to see if a high hit rate was achieved. 4. Feeding back results into the customer database, to carry out the refinement of the rules used for targeting customers with the product. (2) Direct marketing Direct marketing can be found in many industries, applications in financial, retail, telecom are mentioned in this research. Direct marketing is returning as a valuable tool in modern business as mass marketing loses effectiveness (Forcht, 1999).The purpose of direct marketing is to reduce cost and increase efficiency by selling products or services to customers directly instead of the utilization of merchandisers or wholesalers. Direct marketing asks for immediate customer response or purchase rather than long term product image build-up ( 洪 順 慶, 2001). Sales information is sent to certain customers via media such as mail, television, , internet...etc, and customers can response to the company from indicated channels. Direct marketing is a matured business function. Information tools can help sales 16

13 representatives to interpret customers characteristics in previous transactions, and then significant characteristics are chosen to be the accordance of making marketing decisions. So in the next stage, customers conform to those characteristics predefined by sales persons will be classified into the same group, and suitable marketing campaign can be made and assign to this group. There is a four steps approach stated by Pitta (1998) as follows: 1. Identify customers 2. Differentiate each customer 3. Interact with each customer 4. Customize products for each customer With the increase of useful information, enterprise employs information tool to improve the accuracy of the name list and the efficiency of analysis processing. This kind of marketing strategy can focus on typical customers or even provides one to one services, and these customers usually have higher response rate. (3) Segmentation analysis Segmentation is essentially aggregating customers into groups with similar characteristics such as demographic, geographic, or behavioral traits and marketing to them as a group (Bose, 2002). Facing the market with diverse demands, applying market segmentation strategy can increase company s expected return. Much of marketing research focuses on examining how variables such as demographics and socioeconomic status can be used to predict differences in consumption and brand loyalty. The market segment strategy development processes are described as follows ( 洪 順 慶,2001): 1. Define company s over all products and market range 4. Assess each segment s potential 2. Check the basis of market segmentation, segment the market 5. Choose target market segment 3. Describe market segment 6. Develop marketing strategy and position plan Figure 2-1 Market Segment Strategy Development Source: 洪 順 慶, 2001, 行 銷 管 理, 新 陸 書 局 17

14 Segmentation problem should be considered as two different situations: known character parameters and unknown character parameters. Character parameters are known means segmentation analysis deals with customers who have transactional or behavioral records stored in the company database, and the analytic parameters are predefined and are derived from analyzer s interests. The analysis is proposed to validate some hypothesis, since the segment standards have been set up. For example, customers income level affects their purchasing tendency. Another segmentation problem type that character parameters are unknown is usually used in exploratory market research. The measured parameters are not assigned, and the customers are grouped by the similarity with each other naturally. (4) Product mix analysis Product mix analysis is one of the main processes in product management and sales strategy planning. Companies usually own many products to meet different customer s demands and each product has its positional strategy and promotional activities, so product mix analysis is introduced to coordinate these synchronous marketing strategies. The purpose of creating several products in same product area is to exhaust the market size, strengthen company s position, and keep potential competitors out. Since different products owned by same company compete in the same market, the complementary and profitability should be maintained well to maximize the total benefits. Simulation and prediction analysis are often used in product mix analysis. (5) Fraud detection The purpose of fraud detection is to reduce the cost caused by criminal activities. The idea is to mine system audit data for consistent and useful patterns of user behavior, and then keep these normal behaviors in profiles. While a transaction occurs, system will compare current transaction with existed profile to analyze where this transaction is dubious. A dubious transaction means it has high probability to be a fraud, so this transaction should not be accepted and an alarm will be sent to the customer and the business manager. Bose (2006) described a complete fraud detection process as follows: 1. Data cleaning: to purge redundant data for a certain fraud action analysis; 2. Feature selection and extraction: to discover indicators corresponding to changes in behavior indicative of fraud; 3. Modeling: to determine fraud pattern by classifier and predictor; 18

15 4. Fraud action monitoring and prediction: to issue the alarm. (6) Churn analysis Churn management consists of developing techniques that enable firms to keep their profitable customers and it aims at increasing customer loyalty. Churn management (Lejeune, 2001) contains following concepts: 1. Customer acquisition is relative to the measures taken by firms for gaining clients and, this, as efficiently as possible; 2. Customer retention is relative to the measures taken to keep the current customers; 3. Customer extension describes methods to increase the customer return (cross-selling, ); 4. Customer selection concerns the identification of the most profitable customers. The adherents of customer retention argue that retaining customers improves profitability, mainly by reducing the costs incurred in acquiring new customers (Reichheld and Kenny, 1990; Reichheld, 1996; Schmittlein, 1995).The main objective is to maximize the lifetime value of a customer to the organization (Peppard, 2000). This can be split into four needs (Strouse, 1999): 1. Discovery of motives prompting existing customers to leave and of the features characterizing these customers; 2. Ability to increase loyalty and to provide services and products dovetailed with expectations of the targeted clients; 3. Ability to design actions for recapturing profitable customers; 4. Customization of sales and marketing campaigns directed to customers identified as probably profitable and loyal. (7) Credit card application and personal loan rating In financial industry, credit risk is the oldest form of risk in the financial markets. It is crucial for corporate financing and our lives. Banks need to investigate customers' credit histories by loan experts before issuing cash cards and approving credit limits, but the expertise is scarce and difficult to duplicate. Credit rating is even more important task today as financial institutions have been experiencing serious competition during the past few years. It is impossible both in economic and manpower terms to conduct all works with the explosive size growths (Lee et al., 2004). 19

16 In order for banks to make more precise judgments and better decisions, an automatic and scientific evaluation model about customers credit rating should be integrated into a simplified rating process, so that applications for cash cards can be processed quickly and accurately to reduce loss from bad debt. For example, neural networks can be trained to simulate the credit rating process and build credible models. Tools from statistics, data mining, and artificial intelligence (AI), discriminant analysis, factor analysis, data envelope analysis, and artificial neural networks, have all contributed to the progress in credit risk measurement. The process of credit rating can be summarized into three parts: 1. Rating standard decision: sets the criterion of financial risk and credit level. Comparing rating result to those criterions can tell whether the application should be taken or not. 2. Choosing variables: Variables to evaluate credit are materialized by bankers. Credit granting factors include people factor, purpose factor, payment factor, protection factor, perspective factor. These factors are summarized from pointers of historical records and financial reports. 3. Data mining: chooses proper technique to process the analysis. (8) Stock movement prediction Stock movement prediction research can also utilize data mining techniques to build analysis model from historical data (Gavrilov et al, 2000; Lu et al, 1998; Povinelli et al, 1999). The performance of stock market is the result of all participator s collaboration. Investor s behavior has particular principles such as time sequence and is affected by certain factors. Juridical person and investors will actively invest certain industry and periodically turn to another based on the company s strategy or other reasons. The knowledge of sequential phenomenon mentioned above can be mined, so sequential pattern has been performed broadly in this area. Traditionally, the prediction models are developed with statistical and regression formulas, but the combination of machine learning and statistical methods can provide better performance and accuracy. Statistical and regression approaches have long been used to forecast numerical information in financial area and stock market. Machine learning is relatively new technique. Machine learning enables more complex prediction approach and calculation which approximate reality better, hence, it usually produces more accurate prediction results. The basic strategy of stock price movement prediction is based on time (Lu et al, 1998). Each record in the database represents 20

17 one trading day and contains a list of price information. The processes of stock movement prediction collected from literatures can be induced as follows: 1. Determine analytical variables: analysts can try different factors to find the most sensitive ones. 2. Find the pattern of stock movement: use determined variables in previous step to build a model and predict stock movements. (9) Inventory analysis How to develop an effective inventory management method to properly allocate resources and stocks and how to deliver products into customer s hands on time is enterprise s key competence. Retailer can use association analysis to know the causal relationship between sales condition and required products. The causal relationship can be used to build requirement prediction model to forecast the requirements in the future. The complexity and difficulty of related requirements which makes inventory management s efficient going down usually upraises with the amount of items. The support concept in association approach replaces the man-made probability forecasting of pair requirements to get a more objective assessment. The itemsets derived from previous analysis step can be further clustered to know the products sales features and requirement in different periods. Replenishment of stock with this information will become precise, reducing inventory cost and avoiding insufficient inventory at the same time. (10) Charge making Pricing is now a dramatically different area than it has been historically. New software tools give companies a new way of setting, optimizing, and enforcing pricing changes within the organization. With the best tools, an integrated view of customers, their past purchases, benchmarked pricing by segment and size of purchase, relationship data, and comparison of trends over time all are available to provide decision support to field sales representatives, sales managers, marketers and general managers (Davidson and Simonetto, 2005). Because pricing model complexity has increased, the pricing decision making becomes harder today. A company might have ten ways of regular charging, and another twenty ways of promotional charging which cooperating with its partners on many products, so software tool is required to provide an overview and to support pricing decision. 21

18 The process of charge making analysis is summarized from literatures as following contents: 1. Current pricing model evaluation: analyzes current sales condition by considering product mix, customer segmentation, and promotional activities. 2. Finding beneficial opportunity: from the result of pricing model analysis to design marketing campaign. For example, cross-selling analysis can be brought out in this phase. 3. Future pricing model evaluation: predicts the status and total revenue after new campaign has been applied. (11) Link analysis Link analysis is based on a branch of mathematics called graph theory and it has yielded promising results in solving some problems such as analyzing telephone call patterns. Each telephone call is a relationship (link) between two end points, so call patterns are naturally presented as graphs with thousands or millions of nodes and edges. Link analysis is playing two roles in analyzing the cellular phone data (Berry et. al, 1997). The first is the power of visualization. Second, link analysis can apply the concept generated by visualization to larger sets of customers. The power of visualization provides the ability to see some of the graphs representing call patterns makes patterns for things like inertia or influence much more obvious. In the second stage of link analysis, the previous retrieved pattern is applied to the whole transactional database to find other correspondence ones. These chosen customers match certain features and some marketing opportunities may fit to them. The basic search strategy of this problem is to traverse the call network to calculate the inertia or influence for every customer by their calling records, so it is simple in theory but time-consuming in traversing links. Links may suggest important patterns in the data, but the significance of the patterns requires a person for interpretation (Berry et. al, 1997). (12) Production yield analysis A high priority goal for wafer manufacturing is finding the most probable causative factors that discriminate between low yield and high yielding product by quickly examining the historical data already being collected. In Gardner s (2000) case study of Motorola s semiconductor wafer manufacturing problem gives an 22

19 example. Motorola s production analysis team use tool called CorDex, creates a two-dimensional relational topology, called a "cluster map, that best maintains the original 133 dimensional data inter-relationships. A combination of self-organizing map (SOM) neural networks and rule induction is used to identify the critical poor yield factors from normally collected wafer manufacturing data. The SOM neural network algorithm performs a type of multivariate, non-linear regression." Rule Induction is an additional unsupervised data mining algorithm that works synergistically with the cluster map. It generates logical expressions (rules) that identify the data attributes that most discriminate between clusters, thus explaining the clusters. 2.4 Summary Data mining has a large family composed of various algorithms, and the scope is still expanding, because researchers devote to improve the efficiency and accuracy of existed algorithms, new approaches increase with time. Most researches in data mining area focus on improving efficiency and accuracy of single business application, fewer efforts are devoted into the discussion of application s ease of use. The more complex the application is, the larger the gap comes into existence between application and users. We study data mining related applications to draw the concepts and characters, and then propose a selection model to match these business requirements to data mining categories to connect complex data mining concepts with business problems and assists users to choose the best data mining solution. 23

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CHAPTER 1 DATA MINING AND WAREHOUSING CONCEPTS 1.1 INTRODUCTION The past couple of decades have seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation

Sequence for Determining Necessary Data. Wrong: Catalog everything you have, and decide what data is important. Right: Work backward from the solution, define the problem explicitly, and map out the data

Leading the Evolution WHITE PAPER BUSINESS RULES AND GAP ANALYSIS Discovery and management of business rules avoids business disruptions WHITE PAPER BUSINESS RULES AND GAP ANALYSIS Business Situation More

1 CHAPTER 1 INTRODUCTION Exploration is a process of discovery. In the database exploration process, an analyst executes a sequence of transformations over a collection of data structures to discover useful

International Journal of Computer Science and Software Engineering Volume 2, Number 1 (2015), pp. 1-6 International Research Publication House http://www.irphouse.com Dynamic Data in terms of Data Mining